Who are the most influential users in a recommender system?

Collaborative filtering (CF) is a popular method for personalizing product recommendations for e-commerce applications. In order to recommend a product to a user and predict her preference, CF utilizes product evaluation ratings of the like-minded users. This process of finding the like-minded users causes a social network to be formed among all users. In this social network, each link between a couple of users presents an implicit connection between them. Here, there are some users who have more connections with others and are called the most influential users. This paper attempts to model and analyze the behavior of these users by employing data mining techniques. First, the most important features which present a user's influence were selected with a linear regression method, and then, the modeling was performed by a decision tree. Based on our results, the most influential users are users who show more interest to rate more than average number of items with low frequency. Moreover, other most influentials are users who rate in moderation items which have been seen in moderation. In addition, these items are rated with good degree of agreement with other users' rates on the items. We achieved a high accuracy with this model.